How are natural symbol systems best understood? Traditional “symbolic” approaches seek to understand cognition by analogy to highly structured, prescriptive computer programs. Here, we describe some problems the traditional computational metaphor inevitably leads to, and a very different approach to computation (Ramscar, Yarlett, Dye, Denny, & Thorpe, 2010; Turing, 1950) that allows these problems to be avoided. The way we conceive of natural symbol systems depends to a large degree on the computational metaphors we use to understand them, and machine (...) learning suggests an understanding of symbolic thought that is very different to traditional views (Hummel, 2010). The empirical question then is: Which metaphor is best? (shrink)
Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning—in particular, word learning—in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features from a (...) label. This analysis predicts significant differences in symbolic learning depending on the sequencing of objects and labels. We report a computational simulation and two human experiments that confirm these differences, revealing the existence of Feature-Label-Ordering effects in learning. Discrimination learning is facilitated when objects predict labels, but not when labels predict objects. Our results and analysis suggest that the semantic categories people use to understand and communicate about the world can only be learned if labels are predicted from objects. We discuss the implications of this for our understanding of the nature of language and symbolic thought, and in particular, for theories of reference. (shrink)
There is an old joke about a theoretical physicist who was charged with figuring out how to increase the milk production of cows. Although many farmers, biologists, and psychologists had tried and failed to solve the problem before him, the physicist had no trouble coming up with a solution on the spot. “First,” he began, “we assume a spherical cow ... ” [Tenenbaum & Griffiths].